The utility of genomic prediction models in evolutionary genetics

Abstract

Variation in complex traits is the result of contributions from many loci of small effect. Based on this principle, genomic prediction methods are used to make predictions of breeding value for an individual using genome-wide molecular markers. In breeding, genomic prediction models have been used in plant and animal breeding for almost two decades to increase rates of genetic improvement and reduce the length of artificial selection experiments. However, evolutionary genomics studies have been slow to incorporate this technique to select individuals for breeding in a conservation context or to learn more about the genetic architecture of traits, the genetic value of missing individuals or microevolution of breeding values. Here, we outline the utility of genomic prediction and provide an overview of the methodology. We highlight opportunities to apply genomic prediction in evolutionary genetics of wild populations and the best practices when using these methods on field-collected phenotypes.

Genomic prediction of growth in a commercially, recreationally, and culturally important marine resource, the Australian snapper (Chrysophrys auratus)

“…Our data have the potential to determine individual breeding values, but this can be extended to the population level, with broader evolutionary and ecological implications. Estimating a genotype value and its frequency in a population could make it possible to predict how the population will respond to a future environmental event in respect to the phenotypic trait in question ( Hunter et al 2021 ; McGaugh et al 2021 ). This can be an important tool in the restocking of heavily affected populations, either by climatic change or by overfishing.…”

Rapid polygenic adaptation in a wild population of ash trees under a novel fungal epidemic

“…A recent study showed a shift in GEBVs for adult weight in a 35 year data set for Soay sheep, though phenotypic body weight decreased over this time span 20 . Genomic prediction approaches have a wide future applicability in evolutionary studies 21 .…”

“…A recent study showed a shift in GEBVs for adult weight in a 35 year data set for Soay sheep, though phenotypic body weight decreased over this time span 20 . Genomic prediction approaches have a wide future applicability in evolutionary studies 21 The action of natural selection in the wild for increased health of European ash under ash dieback is good news, but whether the rate of change will be sufficient for evolutionary rescue to occur 15 , and whether its end point will be a fully resistant tree, is as yet unknown. The population we studied is fixed for alleles associated with high susceptibility at more than 900 loci; at these loci gene flow from other populations will be essential to introduce beneficial alleles.…”

“…Consequently, much more convincing evidence for the genetic basis of evolutionary responses has been shown empirically for one or a few newly arisen loci of large effect 12,13 than for responses involving many loci of small effect.Population-level whole genome re-sequencing can be used in a strategy to detect the potentially more widespread polygenic responses to selection if two requirements are met: (1) many single nucleotide polymorphisms (SNP) are identified associated with a trait, and (2) temporal change in their frequencies under selective pressure for the trait can be shown. Genomic prediction approaches, developed for agricultural breeding, using genome-wide SNP data to predict individuals' genetic merit for a quantitative trait of interest, could enable us to meet the first requirement 14,15 . To meet the second requirement we must know genome-wide allele frequencies before and after the arrival of a new selective pressure 16 or in different age classes at a single time point 8,[17][18][19][20][21] .…”

Using genomic prediction to detect microevolutionary change of a quantitative trait

“…Genomic prediction [22], is a tool widely used in animal and plant breeding to estimate quantitative genetic parameters from marker data. More recently, genomic prediction has started to become adopted by researchers working on wild populations [23][24][25][26]. It works by exploiting the fact that when marker density is sufficiently high, some typed SNP markers are in linkage disequilibrium (LD) with unknown causal loci, and the contribution of each SNP (and the unknown loci it tags) to phenotypic variation can be estimated.…”